By condensing a program into the weights of a neural network and training it tabla-rasa starting with random weights every generation cycle, it is likely that each produced network will have a unique set of learned parameters. Observing the structure and parameters of a neural network does not provide enough information to understand it's functionality [1]}; as a consequence, it is possible for the nature of neural programs in their condensed form to remain elusive as they propagate through digital space. This becomes increasingly plausible when considering that the most common form of malware detection is signature-based, requiring an exact digital "footprint" within the program's instructions, which in the case of neural programs, lies resident inside the uninterpretable network parameters. Early theoretical analysis claims that the "detection of a virus is shown to be undecidable both by a-priori and runtime analysis, and without detection, cure is likely to be difficult or impossible [1]}." It is also unlikely that all arbitrary neural structures would be flagged, especially as the application cases for neural networks continues to rise, leaving the possibility of camouflaged self-replicating neural programs. These programs could be capable of evolution across generations and intelligent decision making during their lifetime, unlike any malware existing today. It is important to consider and study such possibilities before the potential becomes a reality.
